First-class address quality requires first-class data quality. After all, address data is the foundation for business success, because behind every correct data entry is a valuable customer.
Data quality is also of central importance for decision makers. Because it is on the basis of data that market opportunities are evaluated, negotiations are conducted and decisions are ultimately made.
On top of that, a lack of data quality in the individual process stages not only leads to customers leaving, but also causes high costs.
From the definition of a data quality strategy, to database cleansing, to continuous quality controls, PRODATA guides you to the perfect customer database.
Benefit from a holistic, qualitative data management!
The four steps model of PRODATA will help you to get a holistic qualitative data management.
We analyze your data base and define a target state together. The aim is to get an economical optimum for all involved interfaces.
We construct you a requirement specification (handbook) with all important details.
All existent customer data according to the previously defined criteria will be analyzed, verified and corrected. By request the data can also be enhanced. The current data base will get a new and better data standard.
This process can contain for example the following services:
Due to the coverage of the new data base standard, all leading interfaces (call center, marketing, sales department, accounting department, ERP, etc.) will be lifted to the new data base standard. Different functions will be assembled for example plausibility check-up ,references, scaling and reconciliation). A “contamination” with information that does not fit in the target state will be avoided. If the normed data is qualitatively inadequate, it will be repudiated or qualified.
In this phase it is decided whether the systemic conversion will be on an own data hub or whether the particular systems/interfaces will be integrated fractional.
Those act as an early-warning system and are used to safeguard a consecutive optimized data quality. A contingent readjust of the data accrual is possible in time in order to keep the desired data standard.